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- W4310693152 abstract "Legal information extraction requires identifying and classifying legal elements from specific legal documents. Considering that information extraction is mostly regarded as the first step in natural language understanding (NLU), the quality of legal information extraction results certainly has a great impact on the performance of various legal AI downstream tasks. However, due to the particularity of legal documents, Chinese judicial information extraction datasets are very scarce. In response to this situation, we constructed a dataset for Callenge of AI in Law - Information Extraction V1.0 (CAILIE 1.0). The following two features of CAILIE are worth mentioning: 1) The entity definition focuses on more fine-grained theft document information, providing more interpretability for downstream legal AI. 2) To meet the needs of Chinese judicial practice, we define entity labels with judicial attributes based on natural attribute labels. We implement some classic models on this dataset. The experimental results show that legal information extraction is still challenging and additional research is required for this task to be solved." @default.
- W4310693152 created "2022-12-15" @default.
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- W4310693152 date "2022-01-01" @default.
- W4310693152 modified "2023-09-24" @default.
- W4310693152 title "CAILIE 1.0: A dataset for Challenge of AI in Law - Information Extraction V1.0" @default.
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- W4310693152 doi "https://doi.org/10.1016/j.aiopen.2022.12.002" @default.
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